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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Infrastructure? No Offer Description Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description
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specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision? Then check out the vacancy below and apply for a PhD position in this
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advantage. Solid problem-solving skills and capacity to take the initiative. Fluency in written and spoken English. For more details, please check the Graduate Schools Admission Requirements: https
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PCM composites the RecyWax+ project combines multi-physics modelling approaches and physical experimentation of the thermal properties of the PCM composite. Throughout, theory is validated by synthesis
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where trustworthiness is essential. Today’s explainability methods often do not provide meaningful decision support and, in some cases, can even leak sensitive training data. Models are especially
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/ML) models can offer a solution here. This project aims to determine to what extent AI/ML models based on electrochemical sensor data are able to identify and quantify local forms of corrosion. We
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particular interests and strengths. Your team Your main supervisor will be Matthijs Vákár . You’ll also be supported by secondary supervisors who are experts in machine learning, chosen to match your profile
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description Positive tipping points
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required): Affinity with trauma research, and experience with EEG, R, hierarchical data analysis (e.g., linear mixed models), and computational modeling is preferred. LanguagesENGLISHLevelExcellent Research
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unprecedented real-time visualisation of these processes, yet systematic investigation across diverse rock types and integration with predictive models remains lacking. In this PhD study, you will be performing